As shown above, the historical motivation for understanding and modeling the dynamics and behavior of agricultural systems was primarily to assess and predict future food production and supply. More recently, however, it has become evident that in addition to functioning as entities for production, agricultural systems may also either damage or provide ecological goods and social welfare. Meeting sustainability challenges for agricultural system management requires approaches that assess socioeconomic concerns and environmental impacts integratively. IA is being increasingly used to integrate the numerous dimensions surrounding agroe-cosystem management including the consideration of multiple issues and stakeholders, the key disciplines within and between the human and natural sciences, multiple and cascading scales (both spatially and temporally) of agricultural system behavior, models of the different agricultural system components, and multiple databases (Figure 4). Although there appears to be no universally agreed upon definition in the literature of what constitutes IA, there seems to be widespread agreement that IA:
• is a feedback-driven, interdisciplinary, and participatory (i.e., stakeholder involvement) process;
• is an iterative process of investigation and recommendation that stresses the importance of communication from scientist to decision-makers;
• explicitly accommodates linkages between the natural and human environment, and between research and policy; and
• uses the latest scientific tools including computer models, systems simulation, remotely sensed data, and other forms of information technology to assemble, integrate, and synthesize data from a wide range of sources and across a wide range of spatial and temporal scales.
Agricultural systems around the globe are continuously changing as a result of population demographics, climate fluctuations, and introduction of new agrotechnologies. There is consensus that modeling tools are needed to support sustainability within various agricultural sectors, and even more importantly to enhance the contribution of agricultural systems to sustainable development of societies at large. The integrated assessment modeling (IAM) process attempts to integrate various types of models (e.g., qualitative, quantitative, data, decision
Stakeholders and decision makers
Spatial and temporal scales
Models and data
Conceptual, mechanistic, empirical, etc.
Figure 4 Types of integration to address agroecosystem sustainability issues.
support) into an IA framework. More importantly, in current IAM approaches the earlier forms of systems modeling are being replaced with new integrated models that incorporate a three-pronged approach that considers ecological, social, and economic values when addressing sustainable usage of agricultural resources. Examples of this are recent IA analyses of climate change impacts on whole-farm systems. In these analyses, whole-system agricultural modeling frameworks were combined with a stakeholder-driven participatory process in order to assess potential effects of future climate change on agroecosystem land-use and management patterns. The System for Environmental and Agricultural Modelling; Linking European Science and Society Integrated Framework (SEAMLESS-IF) is an example of an IA tool that uses sustainability indicators (economic, environmental, and social) and agricultural systems evaluation (quantitative models, tools, and databases) to assess and compare alternative agricultural and environmental policy options. A review of the literature shows an increasing number of IAM exercises for solving agricultural problems; however, data availability, uncertainty characterization, and software platform development are key issues requiring additional research in the future.
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